Cloud/Edge Computing for Deep Learning and Big Data Analytics


March 19, 2024


Prof. L. Carnevale (UNIME),

Dr. Mirco Palese (Engineering),

Dr. A. Filograna (Engineering),

Dr. M. Basile (Engineering),

Mr. A. Kaimakamidis (AUTH),

Ms. S. Sebbio (UNIME),

Mr. P. Giannouris (AUTH),

Mr. P. Mentesidis (AUTH),

Content and organization

This short course on Cloud/Edge Computing for Deep Learning and Big Data Analytics provides a comprehensive overview and detailed presentation of advanced technologies utilized in distributed computational systems. Distributed computing plays a critical role in today’s data-driven landscape, enabling the processing of vast amounts of data efficiently and effectively across multiple nodes and locations. Through the distribution of computing tasks across systems, distributed computing amplifies scalability, reliability, and performance, proving indispensable for managing big data analytics, machine learning, and real-time processing tasks.

The course consists of six lectures and one workshop, covering some of the most popular tools necessary for distributed systems. Docker and Kubernetes streamline distributed computing by containerizing applications and automating deployment and management across clusters of servers. The Orion Context Broker facilitates seamless communication and data sharing between distributed systems, while Apache Airflow automates workflow scheduling, enhancing the efficiency of data processing pipelines. Together, these technologies empower organizations to leverage distributed systems fully, driving innovation and maximizing the value of their data. Additionally, a lecture on decentralized DNN application will provide a research-oriented view of multi-agent learning systems. Finally, the workshop will combine all the aforementioned tools into a practical application.

Online Participation via ZOOM

Meeting ID: 928 2622 5947

Passcode: 405011

This short course is technically sponsored  by: TEMA, AIDA, AI. BIG cluster, AI4Media, AI4Europe & SIMAR R&D projects.


Course Duration

8 hours

Course Type

Short Course

Participation terms

CS/ECE/EE/AI students/scientists, engineers as well as AI enthusiasts from other scientific disciplines having the necessary mathematical background are welcomed to register free of charge on a First-Come-First-Serve basis. Please find the Registration Form.
If you are an AIDA Student* already, on top of the above registration, enroll on this course using the button "ENROLL ON THIS COURSE" below, so that this course is included on your AIDA Certificate of Course Attendance, upon successful course participation (attendance of at least 80% of the scheduled course lectures).
*AIDA Students should have been registered in the AIDA system already (they are PhD students or PostDocs that belong only to the AIDA Members list.


08:30-18:30 CET



Modality (online/in person):



The workshop "Big Data Analytics example for Natural Disaster Management" (16:30-18:30 CET) will be delivered exclusively for TEMA partners.

Host Institution
Aristotle University of Thessaloniki

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